Exploiting Kronecker structure in exponential integrators: fast approximation of the action of $\varphi$-functions of matrices via quadrature

TL;DR

This paper introduces a fast, structure-exploiting algorithm for approximating matrix $\

Contribution

It presents a novel quadrature-based method leveraging Kronecker structure to efficiently compute $\\varphi$-functions of matrices, outperforming existing techniques.

Findings

Achieves supergeometric convergence rates.

Orders of magnitude faster than current methods.

Effective in 2D and 3D linear and semilinear problems.

Abstract

In this article, we propose an algorithm for approximating the action of $\varphi-$functions of matrices against vectors, which is a key operation in exponential time integrators. In particular, we consider matrices with Kronecker sum structure, which arise from problems admitting a tensor product representation. The method is based on quadrature approximations of the integral form of the $\varphi-$functions combined with a scaling and modified squaring method. Owing to the Kronecker sum representation, only actions of 1D matrix exponentials are needed at each quadrature node and assembly of the full matrix can be avoided. Additionally, we derive \emph{a priori} bounds for the quadrature error, which show that, as expected by classical theory, the rate of convergence of our method is supergeometric. Guided by our analysis, we construct a fast and robust method for estimating the optimal scaling factor and number of quadrature nodes that minimizes the total cost for a prescribed error tolerance. We investigate the performance of our algorithm by solving several linear and semilinear time-dependent problems in 2D and 3D. The results show that our method is accurate and orders of magnitude faster than the current state-of-the-art.